7 min read
If you ever heard an exasperated teacher tell you the concept you are failing to grasp "isn't rocket science", you'd be forgiven for rolling your eyes at the trite cliché. Not so with today's interviewee David Napoli. He actually knows exactly what rocket science is as a former aerospace engineer.
Now, as a data consultant and highly respected member of the teaching faculty at General Assembly, David uses all of his analytics experience to help create a new generation of data analysts and storytellers.
Welcome to Analysts Assemble, David Napoli.
Tell us a bit about yourself, how did you get into the data space and what does your data journey look like so far?
My data journey could probably be described as “unique”, having started nearly 25 years ago as an aerospace engineer – more specifically a propulsion subsystems engineer – performing modeling of second stage propulsion systems by coding in Fortran … with a little Excel modeling thrown in here and there.
I bounced from project to project for a number of years as funding was lost, and after the eighth time of that happening, I decided to give up on that dream (I had – and still have – an interest in the space field since I was 10) and began a search for a new career path.
Well, that was roughly 20 years ago, where I was fortunate enough to be offered the opportunity to make the leap from “rocket scientist” to Health Care Actuary … and that journey has since taken me through several other roles, such as Lead Decision Support Analyst, Lead Statistician, Research Manager, and more recently, Director of Business Intelligence.
As a Director, it was my responsibility to set the foundation for Data Literacy across the organization, as well as develop and implement the roadmap for Data Maturity for both my team and the organization as a whole to follow.
Besides implementing a Data Analytics Maturity Model for the organization to use to monitor and evaluate its analytic development over time, I also instituted a Business Intelligence Competency Center (BICC) and a Data Governance Committee (DGC) to support the data literacy efforts.
The BICC was established to provide a platform for analytic staff to convene and share knowledge and insights, as well as institute a formal setting to develop data lineage, data dictionaries, and policies and procedures to ensure repeatable processes were established.
The DGC then formalized oversight and direction at the executive level, and provided the pathway to establish formal data literacy trainings, which I developed and led, to ensure the organization would grow in how to use information, how to ask meaningful questions of data, how to read visualizations, and more.
I have since moved on from the Director role to a new stage of my career journey, which I am hopeful will continue for many years to come, and blossom for both myself and others … but we’ll get to that in the next question.
What’s a typical day look like for you in your current data role? Which tools and languages do you use? Big team/small team/lone wolf? Remote/office based/co-working space?
For the past several years, I no longer have a typical day. Since I left my last full-time role, I have branched into part-time independent consulting and part-time teaching.
My consulting efforts span the entirety of the analytics space, from data management and governance – establishing ETL processes, MDM, data models, and warehouses – to developing statistical and predictive models – IBNR estimations and risk adjustment methods, for example – to creating visual exploratory analytics, dashboards, and more elaborate visual storytelling of programs and initiatives.
Where my true career passion lies, however, is in teaching.
I came across this career path somewhat serendipitously roughly four years ago, during essentially a hallway conversation at a consulting gig at the time. The individual I was conversing with was an occasional speaker at General Assembly, an organization focused on education and career transformation, specializing in the areas of UX & Design, Marketing, Career Development, Coding, and, of course, Data.
The local (Denver) organization was looking for instructors in the area of Data Analytics, as they wished to expand their offerings, and this individual said they would introduce me to an Instructor Manager there if I were interested.
While I had developed and hosted several “lunch and learn” sessions related to analytics over the years at several organizations, I had not given serious thought to teaching as a career pathway, despite how much I had enjoyed those previous brown bag sessions I hosted.
So, needless to say, I enthusiastically said yes.
I ended up speaking with the local Instructor Manager the following week, and was offered the opportunity to teach an evening “Introduction to Data Analytics” class a few weeks later … which went better than I could have ever anticipated. Being able to share my passion for data analytics and visualization with others, and see their own excitement in learning and growth in their analytics capabilities was beyond rewarding.
The feedback I received from that first class was overwhelmingly positive, which led to a second class, and then a third, and… well, now four years in, I have been selected as a member of General Assembly’s Distinguished Faculty program, where I lead the 10-Week Data Analytics course instruction, and also host classes in Data Visualization, Data Storytelling, as well as a concept I am currently developing called Data Dexterity.
As these teaching roles are only part-time, I do what I can to “fill in the gaps” with consulting efforts I am able to secure.
I have used a wide range of tools during my analytics career. My current courses focus on Excel, SQL, and Tableau. I have been an Excel user for almost 30 years, so I know that tool inside and out. SQL is not too far behind in my use, at around 25 years.
Tableau is much newer to me by comparison, having put that in place for an organization a bit over six years ago. I also use Power BI – having used this tool since its first incarnation called Power View when it was originally available in Excel 2010 – and have explored Qlik and Looker.
Coming from a statistical background (I have all but my dissertation in Health Services Research, with a focus on Biostatistics), I also work in SAS and R. Once I am able to carve out some time, Python is on my list to learn.
I am always keeping up on the latest analytics and visualization tools to be aware of and explore, and there is certainly no shortage of them!
You have an excellent daily analytics newsletter that rounds up a wide range of stories from around the data world. You are also very active on Twitter. How important do you think it is for data professionals, at all stages of their career, to share publicly what they are doing and learning?
I can not stress any more strongly how important it is to share with others your thoughts, ideas, and passions.
The whole concept of “if you and I exchange a dollar, we each have a dollar, but if you and I exchange an idea, we now both have two ideas” is absolutely true.
Establishing relationships with others, through sharing and learning together, begets further understanding of the topics at hand by both parties, fosters a deeper passion for learning and sharing, and often will spark interest and excitement in others who initially are only peripherally involved so they join in on the dialogue and continue their learning and further the knowledge sharing.
The number of valuable perspectives in the fields of data analytics and visualization is vast, and one of the hopes of my newsletter is to offer others a source which provides a steady stream of those different perspectives, providing a breadth and depth of knowledge and experiences to learn and be inspired from.
I will never stop being curious on what else there is out there being discussed, explored, evaluated, and communicated in data analytics and visualization.
What can we be doing better which could lead to better insights to help humanity?
What methods from other fields have I yet to be exposed to which could prove fruitful to efforts I am or will be undertaking?
What concepts and methods do others feel are foundational I should then incorporate in my teachings so others can grow above and beyond my own standing?
The list of such reasons could go on and on, but it is all focused on how much data analytics and visualization is a passion of mine, and I will do all I can to share my passion and understandings with others, with hopes of sparking a shared enthusiasm in others and finding those who wish to join me in this intellectual journey.
Where do you see your own data career going next? Building on your technical skills or moving into a more management-based role?
As I had already been in management roles for 10+ years and chose to “move on”, I wish to continue my current path and further my efforts in teaching, and promoting data analytics, governance, visualization, ethics, and the human element.
I do this through my newsletter, giving workshops, speaking at events, and hopefully soon, establishing my own website (I’ve had the URL for around a year … I just need to figure out the setup and maintenance concerns and carve out the time to get it up and running) and finally begin writing a book.
As the concept of “uncertainty is information” has been a focal point of mine during my career the past 20 years, I had originally ruminated on putting together a book to explore and explain methods of visually displaying uncertainty in health care information.
Since that original concept came to life in my mind, I have reconsidered the topic and focus of the book, and while I still wish to include clarification around the methods of displaying uncertainty, I am now leaning towards a more broad publication, one in which I step through the tools and concepts which assisted me in growing and advancing from entry-level analyst to lead and beyond.
I hope others would find value in the details and learnings of such a journey.
If you had a list of “best-kept-secrets” (websites, books, coaches) that have helped you, which would you recommend?
Anything I find valuable in data analytics and visualization spaces, I share through my newsletter as well as in the classes I teach. There are far, FAR too many to choose from to list here … if I attempted to cull my list down, I would be here until the Knicks are finally good again (two winning seasons since my kiddo was born … who is now a sophomore in high school).
I will mention one book which not many in the data analytics space may be aware of, and this is “The Book: Playing the Percentages in Baseball”, by Andrew Dolphin, Mitchel Lichtman, and Tom Tango.
Although the topic is obviously sabermetrics – one of my side analytical passions – the methods explored and explained in this book are widely applicable across industries and the analytical lessons are invaluable to all in analytical fields.
What is the number one piece of advice you give to aspiring data analysts?
Be intellectually curious and analytically skeptical … OK, I know, that’s two, but they are two sides of the same coin.
There are certainly more elements I would want to touch on – data ethics, data visualization, and data humanism immediately come to mind – but those are for another time and lengthy discussion.
Where can readers find you online?